Temporal effects in trend prediction: identifying the most popular nodes in the future

Prediction is an important problem in different science domains. In this paper, we focus on trend prediction in complex networks, i.e. to identify the most popular nodes in the future. Due to the preferential attachment mechanism in real systems, nodes' recent degree and cumulative degree have been successfully applied to design trend prediction methods. Here we took into account more detailed information about the network evolution and proposed a temporal-based predictor (TBP). The TBP predicts the future trend by the node strength in the weighted network with the link weight equal to its exponential aging. Three data sets with time information are used to test the performance of the new method. We find that TBP have high general accuracy in predicting the future most popular nodes. More importantly, it can identify many potential objects with low popularity in the past but high popularity in the future. The effect of the decay speed in the exponential aging on the results is discussed in detail

Current rectification by asymmetric molecules: An ab initio study

We study current rectification effect in an asymmetric molecule HOOC-C$_6$H$_4$-(CH$_2$)$_n$ sandwiched between two Aluminum electrodes using an {\sl ab initio} nonequilibrium Green function method. The conductance of the system decreases exponentially with the increasing number $n$ of CH$_2$. The phenomenon of current rectification is observed such that a very small current appears at negative bias and a sharp negative differential resistance at a critical positive bias when $n\ge 2$. The rectification effect arises from the asymmetric structure of the molecule and the molecule-electrode couplings. A significant rectification ratio of $\sim$38 can be achieved when $n=5$.Comment: to appear in J. Chem. Phy